Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	17,594
2	New Jersey	15,755
3	Massachusetts	11,383
4	Rhode Island	10,808
5	Connecticut	9,470
6	District of Columbia	9,052
7	Louisiana	6,843
8	Delaware	6,741
9	Illinois	6,244
10	Maryland	5,538
11	Michigan	4,758
12	Pennsylvania	4,730
13	Nebraska	4,433
14	South Dakota	4,085
15	Iowa	3,921
16	Indiana	3,733
17	Colorado	3,437
18	Mississippi	3,250
19	Georgia	3,056
20	Virginia	2,937
21	Kansas	2,458
22	New Mexico	2,417
23	Washington	2,403
24	New Hampshire	2,324
25	Tennessee	2,258
26	Ohio	2,120
27	Minnesota	2,092
28	Alabama	2,072
29	Nevada	1,997
30	North Dakota	1,991
31	Utah	1,985
32	Florida	1,907
33	Wisconsin	1,789
34	California	1,759
35	Missouri	1,633
36	Arizona	1,563
37	South Carolina	1,513
38	Kentucky	1,501
39	Vermont	1,484
40	North Carolina	1,436
41	Texas	1,413
42	Arkansas	1,339
43	Idaho	1,265
44	Oklahoma	1,165
45	Wyoming	1,155
46	Maine	1,087
47	Oregon	779
48	West Virginia	763
49	Puerto Rico	706
50	Alaska	519
51	Hawaii	440
52	Montana	429

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	District of Columbia	231
2	Rhode Island	211
3	South Dakota	177
4	New Jersey	168
5	Maryland	159
6	Delaware	155
7	Massachusetts	151
8	Illinois	142
9	Nebraska	128
10	Connecticut	126
11	New York	110
12	Virginia	106
13	Minnesota	101
14	Iowa	96
15	Pennsylvania	79
16	Indiana	74
17	Louisiana	68
18	Mississippi	65
19	Colorado	62
20	New Mexico	62
21	Tennessee	55
22	Alabama	52
23	New Hampshire	52
24	Ohio	50
25	Wisconsin	47
26	Kansas	46
27	Utah	46
28	Georgia	42
29	California	41
30	Michigan	40
31	North Dakota	40
32	Arizona	39
33	North Carolina	37
34	Texas	35
35	Washington	33
36	Arkansas	32
37	Kentucky	30
38	Florida	27
39	South Carolina	27
40	Missouri	24
41	Maine	21
42	Nevada	19
43	Oregon	17
44	Oklahoma	15
45	Wyoming	14
46	Idaho	10
47	Puerto Rico	10
48	West Virginia	8
49	Vermont	3
50	Alaska	1
51	Hawaii	0
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,388
2	New Jersey	1,048
3	Connecticut	843
4	Massachusetts	741
5	Louisiana	482
6	District of Columbia	464
7	Michigan	459
8	Rhode Island	405
9	Pennsylvania	300
10	Maryland	278
11	Illinois	274
12	Delaware	231
13	Indiana	228
14	Colorado	171
15	Mississippi	146
16	Georgia	133
17	Washington	125
18	Ohio	116
19	Minnesota	104
20	Nevada	101
21	New Mexico	99
22	Virginia	99
23	New Hampshire	97
24	Iowa	85
25	Vermont	84
26	Missouri	83
27	Alabama	82
28	Florida	80
29	Arizona	74
30	Kentucky	72
31	California	71
32	Wisconsin	70
33	Oklahoma	69
34	South Carolina	67
35	Kansas	59
36	North Carolina	54
37	Nebraska	51
38	Maine	48
39	North Dakota	47
40	Idaho	39
41	Texas	39
42	South Dakota	38
43	Tennessee	36
44	Puerto Rico	35
45	Arkansas	31
46	West Virginia	31
47	Oregon	30
48	Utah	21
49	Montana	14
50	Hawaii	12
51	Wyoming	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Massachusetts	19
2	New Jersey	13
3	Connecticut	12
4	District of Columbia	11
5	New York	11
6	Rhode Island	9
7	Louisiana	6
8	Maryland	6
9	Michigan	6
10	Illinois	5
11	Delaware	4
12	Indiana	4
13	New Mexico	4
14	Minnesota	3
15	Pennsylvania	3
16	Iowa	2
17	Mississippi	2
18	New Hampshire	2
19	Alabama	1
20	Arizona	1
21	California	1
22	Colorado	1
23	Florida	1
24	Georgia	1
25	Kentucky	1
26	Missouri	1
27	Nebraska	1
28	Nevada	1
29	North Carolina	1
30	North Dakota	1
31	Ohio	1
32	South Carolina	1
33	South Dakota	1
34	Virginia	1
35	Washington	1
36	Wisconsin	1
37	Alaska	0
38	Arkansas	0
39	Hawaii	0
40	Idaho	0
41	Kansas	0
42	Maine	0
43	Montana	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Tennessee	0
48	Texas	0
49	Utah	0
50	Vermont	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	120,790	1	99
Dakota	Nebraska	72,506	2	99
Lincoln	Arkansas	72,251	3	99
Nobles	Minnesota	58,671	4	99
Cass	Indiana	40,489	5	99
Davidson	Tennessee	5,329	239	92
Richland	South Carolina	2,691	575	81
Pierce	Washington	1,898	797	74
Orange	California	1,142	1186	62
York	South Carolina	861	1473	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Randolph	Georgia	3,098	1	99
Terrell	Georgia	2,696	2	99
Early	Georgia	2,552	3	99
Nassau	New York	1,787	4	99
Essex	New Jersey	1,785	5	99
Richland	South Carolina	137	459	85
Pierce	Washington	69	772	75
Davidson	Tennessee	50	933	70
Orange	California	24	1266	59
York	South Carolina	18	1367	56

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.